期刊
EARTH
卷 4, 期 3, 页码 552-569出版社
MDPI
DOI: 10.3390/earth4030029
关键词
class; climate change; education; GIS; growing degree day; Koppen; tutorial
Climate classifications provide inference about general vegetation types based on climate visualization. This study incorporates temperature and aridity information into all climate classes and adds a hypertropical class to better detect warming and drying. By comparing current climate to past climate, the study reveals that the hypertropical class has experienced the greatest change in area and the greatest increase in percentage of arid classes.
Climate classifications supply climate visualization with inference about general vegetation types. The Koppen classification system of thermal classes and an arid class is widely used, but options are available to strengthen climate change detection. For this study, I incorporated temperature and aridity information into all climate classes to isolate climate change, added a hypertropical class to better detect warming and drying in tropical zones, and developed a consistent ruleset of thermal classes with one temperature variable for streamlined application, yet maintained primary Koppen thermal classes. I compared climate currently to 6000 years ago (ka; Mid-Holocene) and 22 ka (Last Glacial Maximum) worldwide. Growing degree days > 0 degree celsius was the most efficient variable for modeling thermal classes. Climate classes based on growing degree days matched 86% of Koppen thermal classes. Current climate shared 80% and 23% of class assignments with the Mid-Holocene and Last Glacial Maximum, respectively, with dry conditions shifting to the tropical and hypertropical classes under current climate. Contributing to our understanding of global environmental change, this classification demonstrated that the hypertropical class experienced the greatest change in area since 6 ka and the second greatest change in area since 22 ka, and the greatest increase in percentage arid classes during both intervals. The added hypertropical class with aridity information delivered sensitive detection of warming and drying for relevant climate classes under climate change.
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